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  • 1.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Mazidi, Peyman
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical University, Madrid,.
    Herre, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models2017In: European Energy Market (EEM), 2017 14th International Conference on the, IEEE conference proceedings, 2017Conference paper (Refereed)
    Abstract [en]

    Energy forecasting provides essential contribution tointegrate renewable energy sources into power systems. Today,renewable energy from wind power is one of the fastest growingmeans of power generation. As wind power forecast accuracygains growing significance, the number of models used forforecasting is increasing as well. In this paper, we propose anautoregressive (AR) model that can be used as a benchmarkmodel to validate and rank different forecasting models andtheir accuracy. The presented paper and research was developedwithin the scope of the European energy market (EEM) 2017wind power forecasting competition.

  • 2.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Mazidi, Peyman
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems. Loyola.Tech, Loyola Andalucia University, Seville, Spain.
    Herre, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Neural networks for GEFCom2017 probabilistic load forecasting2019In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 35, no 4, p. 1409-1423Article in journal (Refereed)
    Abstract [en]

    This report describes the forecasting model which was developed by team "4C" for the global energy forecasting competition 2017 (GEFCom2017), with some modifications added afterwards to improve its accuracy. The model is based on neural networks. Temperature scenarios obtained from historical data are used as inputs to the neural networks in order to create load scenarios, and these load scenarios are then transformed into quantiles. By using a feature selection approach that is based on a stepwise regression technique, a neural network based model is developed for each zone. Furthermore, a dynamic choice of the temperature scenarios is suggested. The feature selection and dynamic choice of the temperature scenarios can improve the quantile scores considerably, resulting in very accurate forecasts among the top teams.

  • 3. Du, Mian
    et al.
    Yi, Jun
    Mazidi, Peyman
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Cheng, Lin
    Guo, Jianbo
    A Parameter Selection Method for Wind Turbine Health Management through SCADA Data2017In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 10, no 2, article id 253Article in journal (Refereed)
    Abstract [en]

    Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine's condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copula-based mutual information estimation method (ECMI) is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

  • 4.
    Mazidi, Peyman
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems. Comillas Pontifical University.
    From Condition Monitoring to Maintenance Management in Electric Power System Generation with focus on Wind Turbines2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    With increase in the number of sensors installed on sub-assemblies of industrial components, the amount of data collected is rapidly increasing. These data hold information in the areas of operation of the system and evolution of health condition of the components. Therefore, extracting the knowledge from the data can bring about significant improvements in the aforementioned areas.

    This dissertation provides a path for achieving such an objective. It starts by analyzing the data at the sub-assembly level of the components and creates four frameworks for analysis of operation and maintenance (O&M) for past, present and future horizons at the component level. These frameworks allow improvement in operation, maintenance planning, cost reduction, efficiency and performance of the industrial components. Next, the dissertation evaluates whether such models can be linked with system level analysis and how providing such a link could provide additional improvements for system operators. Finally, preventive maintenance (PM) in generation maintenance scheduling (GMS) in electric power systems is reviewed and updated with recent advancements such as connection to the electricity market and detailed implementation of health condition indicators into the maintenance models. In particular, maintenance scheduling through game theory in deregulated power system, for offshore wind farm (OWF) and an islanded microgrid (MG) are investigated.

    The results demonstrate improvements in reducing cost and increasing profit for the market agents and system operators as well as asset owners. Moreover, the models also deliver an insight on how direct integration of the collected operation data through the developed component level models can assist in improving the operation and management of maintenance for the system.

  • 5.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Bertling, Lina
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Sanz-Bobi, Miguel A.
    Performance Analysis and Anomaly Detection in Wind Turbines based on Neural Networks and Principal Component Analysis2017Conference paper (Refereed)
    Abstract [en]

    This paper proposes an approach for maintenancemanagement of wind turbines based on their life. The proposedapproach uses performance analysis and anomaly detection(PAAD) which can detect anomalies and point out the originof the detected anomalies. This PAAD algorithm utilizes neuralnetwork (NN) technique in order to detect anomalies in theperformance of the wind turbine (system layer), and then appliesprincipal component analysis (PCA) technique to uncover theroot of the detected anomalies (component layer). To validatethe accuracy of the proposed algorithm, SCADA data obtainedfrom online condition monitoring of a wind turbine are utilized.The results demonstrate that the proposed PAAD algorithm hasthe capability of exposing the cause of the anomalies. Reducingtime and cost of maintenance and increasing availability and inreturn profits in form of savings are some of the benefits of theproposed PAAD algorithm.

  • 6.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical University.
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Sanz Bobi, Miguel Angel
    Comillas Pontifical University.
    Wind Turbine Prognostics and Maintenance Management based on a Hybrid Approach of Neural Networks and Proportional Hazards Model2016In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078Article in journal (Refereed)
    Abstract [en]

    This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines (WTs) through large amounts of collected data from the supervisory control and data acquisition (SCADA) system. The objectives of the proposed approach are to provide a stress condition model for health monitoring, to assess the WT’s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. At first, several statistical techniques, namely principal component analysis, Pearson, Spearman and Kendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assess the data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by an artificial neural network which performs condition monitoring analysis. Then, a model based on the mathematical form of a proportional hazards model is developed where it represents the stress condition of the WT. Finally, those two models are jointly employed in order to analyze the overall performance of the WT over the study period. Several cases are analyzed with five-year SCADA data and maintenance information is utilized to develop and validate the proposed approach.

  • 7.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Sanz-Bobi, Miguel A.
    Comillas Pontifical University.
    Anomaly Detection and Performance Analysis in Wind Turbines through Neural Networks2015Conference paper (Other academic)
  • 8.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical University.
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Sanz-Bobi, Miguel A.
    Comillas Pontifical University.
    Wind Turbine Prognostics and Maintenance Management based on a Hybrid Approach of Neural Networks and Proportional Hazards Model2017In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078, Vol. 231, no 2, p. 121-129Article, review/survey (Refereed)
    Abstract [en]

    This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines(WT) through large amounts of collected data from supervisory control and data acquisition (SCADA) system. Theobjectives of the proposed approach are: to provide a stress condition model for health monitoring, to assess the WT’smaintenance strategies, and to provide recommendations on current maintenance schemes for future operations ofthe wind farm. At first, several statistical techniques, namely Principal component analysis, Pearson, Spearman andKendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assessthe data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by anartificial neural network which performs condition monitoring analysis. Then, a model based on mathematical form ofProportional hazards model is developed where it represents stress condition of the WT. Finally, those two modelsare jointly employed in order to analyze the overall performance of the WT over the study period. Several cases areanalyzed with a five-year SCADA data and maintenance information is utilized to develop and validate the proposedapproach.

  • 9.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical University, Spain.
    Bobi, Miguel A. Sanz
    Shayesteh, Ebrahim
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Impact of health indicators on maintenance management and operation of power systems2017In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078, Vol. 231, no 6, p. 716-731Article in journal (Refereed)
    Abstract [en]

    This article proposes a maintenance management and risk reduction approach. The approach introduces two reliability-based indexes called condition indicator and risk indicator. Condition indicator is a unit-less parameter that comes directly from monitored condition of a component and converts the categorical condition into a numerical value. Risk indicator in megawatt represents the risk imposed by the health of a component onto the system. To demonstrate application of the indicators, they are implemented through an hourly network constraint unit commitment problem and applied in a test system where the analysis of impact of condition of the generators to the operation is the new contribution. The results demonstrate how addition of such indicators will impact the operation of the grid and maintenance scheduling. The results show the benefit for the system operator as the overall failure risk in the system is taken into account, and the benefit for the asset owner as the direct impact of the maintenance to be carried out can be investigated. Two of the main outcomes of the maintenance management and risk reduction approach are as follows: asset owners can analyze their maintenance strategies and evaluate their impacts in the maintenance scheduling, and system operators can operate the grid with higher security and lower risk of failure.

  • 10.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES).
    Du, Mian
    KTH. China Electric Power Research Institute.
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Sanz Bobi, Miguel A.
    Comillas Pontificia Universidad.
    A Performance and Maintenance Evaluation Framework for Wind Turbines2016Conference paper (Refereed)
    Abstract [en]

    In this paper, a data driven framework forperformance and maintenance evaluation (PAME) of windturbines (WT) is proposed. To develop the framework, SCADAdata of WTs are adopted and several parameters are carefullyselected to create a normal behavior model. This model which isbased on Neural Networks estimates operation of WT andaberrations are collected as deviations. Afterwards, in order tocapture patterns of deviations, self-organizing map is applied tocluster the deviations. From investigations on deviations andclustering results, a time-discrete finite state space Markov chainis built for mid-term operation and maintenance evaluation.With the purpose of performance and maintenance assessment,two anomaly indexes are defined and mathematically formulated.Moreover, Production Loss Profit is defined for PreventiveMaintenance efficiency assessment. By comparing the indexescalculated for 9 WTs, current performance and maintenancestrategies can be evaluated, and results demonstrate capabilityand effectiveness of the proposed framework.

  • 11.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Du, Mian
    China Electric Power Research Institute.
    Sanz-Bobi, Miguel A.
    Comillas Pontifical University.
    Simulation Model based on Reliability and Maintenance of a Component and Their Effect on Cost2016Conference paper (Refereed)
    Abstract [en]

    Maintenance actions are importantactivities carried out by utilities to maintain theoperability, reliability and sustainability of systems.These actions are mainly divided into two categories,corrective and preventive with different degrees, e.g.as-good-as-new, as-bad-as-old, imperfect. In this paper,a general flexible maintenance model is created thatintegrates both corrective and preventive maintenanceactions as well as their maintenance degree. Themodel incorporates a novel parameter, maintenanceefficiency, which inherently influences maintenanceactions and the behavior of the component under study.Impact of each maintenance action are evaluated andobserved by considering several implemented indices,e.g. expenses, reliability etc. For input data,information on failure history of the componentsuffice. The model is implemented and run in ARENAsoftware and the study presented shows accurately therelation and impact of employed variables.

  • 12.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical Unviersity.
    Mian, Du
    Bertling, Lina
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Sanz Bobi, Miguel Angel
    Comillas Pontifical University.
    Health Condition Model for Wind Turbine Monitoring through Neural Networks and Proportional Hazard Models2017In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078, Vol. 231, no 5Article in journal (Refereed)
    Abstract [en]

    In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

  • 13.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Mian, Du
    China Electric Power Research Institute.
    Sanz-Bobi, Miguel A.
    Comillas Pontifical University.
    A Comparative Study of Techniques Utilized in Analysis of Wind Turbine Data2016Conference paper (Refereed)
    Abstract [en]

    Power produced by a wind turbine is dependent on many factors with different importance degrees. Knowing the main factors can be found by a thorough analysis of all the factors and their correlation and impact on the main output, active power produced by the wind turbine. Therefore, it is important to monitor the performance of the wind turbines in order to minimize the operation and maintenance costs by pointing out abnormalities. This paper analyzes the main factors affecting active output power of a wind turbine which are Gearbox Temperature, Pitch Angle, Rotor Speed and Wind Speed. The data monitored and measured is for over a 12-month period. Several techniques, Kohonen Maps, Multilayer Perceptron, Decision Trees and Rough Sets, are applied to these data. The objective is to show a comparison of different techniques, their positive and negative points and give the reader the ability to choose the best technique for the study based on the their advantages and disadvantages. For the assessment of data, MATLAB and WEKA software are utilized. Each study presents its accuracy based on the output error.

  • 14.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Sanz Bobi, M. A.
    Strategic maintenance scheduling in an islanded microgrid with distributed energy resources2017In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 148, p. 171-182Article in journal (Refereed)
    Abstract [en]

    This paper addresses passive and active preventive maintenance scheduling in an islanded microgrid with storage and renewable energy sources. At first, under a centralized framework, a single-level cost-minimization formulation for passive maintenance scheduling is developed and used as a benchmark in the operation. An independent microgrid operator is responsible for the operation in this framework. Then, through a bi-level formulation, the active maintenance scheduling and operation is carried out with profit-maximization objective. These two developed frameworks provide the houses with opportunity to earn profit and the regulator and the operator to analyze the performance of the system. The bi-level formulation is transformed into a single-level problem through Karush–Kuhn–Tucker conditions. Furthermore, the proposed model provides the capability of incorporating condition monitoring data into the operation. The model is validated through a test system and the outcomes demonstrate the advantages, applicability and challenges of utilizing the proposed model.

  • 15.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Sanz-Bobi, Miguel A.
    Comillas Pontifical University.
    Implementation of Risk in Generation Planning2015Conference paper (Other academic)
  • 16.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Sreenivas, GN
    Jawaharlal Nehru Technological University (JNTUH).
    Reliability Assessment of A Distributed Generation Connected Distribution System2013Conference paper (Refereed)
    Abstract [en]

    Distributed Generation (DG) plays an important role in different regions of a power system. DG provides an alternative to the traditional electricity sources i.e. oil, gas, coal, water, etc. and can also be used to enhance the current electrical system. DG distribution is likely to improve the reliability of a power distribution system by at least partially minimizing the chance of power interruptions to customers due to loss of utility generators or due to faults in transmission and distribution lines/equipments. In this paper, a typical distribution system is considered and to show the reliability enhancement of the system, different components (fuses, disconnects, DGs) are step by step taken into account and added to the system in five cases. Analytical methodology is used for the analysis. The results demonstrate that DG does improve the reliability of the distribution system.

  • 17.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Research Institute for Technology (IIT), Comillas Pontifical University, Madrid 28015, Spain.
    Tohidi, Yaser
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Ramos, Andres
    Sanz-Bobi, Miguel A.
    Profit-maximization generation maintenance scheduling through bi-level programming2018In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 264, no 3, p. 1045-1057Article in journal (Refereed)
    Abstract [en]

    This paper addresses the generation maintenance scheduling (GMS) dilemma in a deregulated power system. At first, under a centralized cost minimization framework, a GMS is formulated and set as the benchmark (cost-minimization GMS). Then, the cost-minimization is changed into a profit-maximization problem of generation companies (GENCOs) and the GMS is developed as a bi-level optimization. Karush-Kuhn-Tucker conditions are applied to transform the bi-level into a single-level mixed-integer linear problem and subsequently, Nash equilibrium is obtained as the final solution for the GMS under a deregulated market (profit-maximization GMS). Moreover, to incorporate reliability and economic regulatory constraints, two rescheduling signals (incentive and penalty) are considered as coordination processes among GENCOs and independent system operators. These signals are based on energy-not-supplied and operation cost, and ensure that the result of profit-maximization GMS is in the given reliability and social cost limits, respectively. These limits are obtained from the cost-minimization GMS. Lastly, the model is evaluated on a test system. The results demonstrate applicability and challenges in GMS problems. 

  • 18.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical University, Spain.
    Tohidi, Yaser
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Sanz-Bobi, Miguel A.
    Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System2017In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 10, no 3, article id 313Article in journal (Refereed)
    Abstract [en]

    This paper proposes a model for strategic maintenance scheduling of offshore wind farms (SMSOWF) in a deregulated power system. The objective of the model is to plan the maintenance schedules in a way to maximize the profit of the offshore wind farm. In addition, some network constraints, such as transmission lines capacity, and wind farm constraints, such as labor working shift, wave height limit and wake effect, as well as unexpected outages, are included in deterministic and stochastic studies. Moreover, the proposed model provides the ability to incorporate information from condition monitoring systems. SMSOWF is formulated through a bi-level formulation and then transformed into a single-level through Karush-Kuhn-Tucker conditions. The model is validated through a test system, and the results demonstrate applicability, advantages and challenges of harnessing the full potential of the model.

1 - 18 of 18
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